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Regional Sustainability >

2024 , Vol. 5 >Issue 3: 100164

DOI: https://doi.org/10.1016/j.regsus.2024.100164

  • Guzel SALIMOVA , a, * ,
  • Gulnara NIGMATULLINA a ,
  • Gamir HABIROV a ,
  • Alisa ABLEEVA a ,
  • Rasul GUSMANOV b
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收稿日期: 2023-07-25

  修回日期: 2024-03-12

  录用日期: 2024-08-26

  网络出版日期: 2025-08-14

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Employment and development levels in rural areas of the Russian Federation

  • Guzel SALIMOVA , a, * ,
  • Gulnara NIGMATULLINA a ,
  • Gamir HABIROV a ,
  • Alisa ABLEEVA a ,
  • Rasul GUSMANOV b
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  • aDepartment of Finance, Analysis and Accounting Technologies, Bashkir State Agrarian University, Ufa, 450001, the Russian Federation
  • bDepartment of Economics and Management, Bashkir State Agrarian University, Ufa, 450001, the Russian Federation
* E-mail address: (Guzel SALIMOVA).

Received date: 2023-07-25

  Revised date: 2024-03-12

  Accepted date: 2024-08-26

  Online published: 2025-08-14

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本文引用格式

Guzel SALIMOVA , Gulnara NIGMATULLINA , Gamir HABIROV , Alisa ABLEEVA , Rasul GUSMANOV . [J]. Regional Sustainability, 2024 , 5(3) : 100164 . DOI: 10.1016/j.regsus.2024.100164

Abstract

The development of agro-industrial complex is important for ensuring national food security and national health. The development of rural areas is subject to the development of agriculture and local infrastructure, as well as the availability of various services. This study selected 15 indicators in 2021 to analyze the employment and development levels in rural areas of 71 regions of the Russian Federation using the analytical grouping method. The results indicated that 20 regions (Group 1) had the highest percentage of rural population (33.10%). The percentage of population engaged in agriculture had the highest value (12.40%) in 31 regions (Group 2). Moreover, 20 regions (Group 3) had the highest investments in fixed assets at the expense of municipal budget (11.80 USD/person). Increasing the investments in fixed assets carried out from the budget of the municipality can improve the employment level in rural areas. Then, we used cluster analysis to divide 14 regions of the Volga Federal District in the Russian Federation into 3 clusters. Cluster 1 covered Kirov Region and Republic of Mari El; Cluster 2 included Ulyanovsk Region, Saratov Region, Nizhny Novgorod Region, Perm Territory, Orenburg Region, Chuvash Region, and Republic of Mordovia; and Cluster 3 contained Republic of Tatarstan, Samara Region, Udmurtian Republic, Penza Region, and Republic of Bashkortostan. Results indicated that the 2 regions of Cluster 1 need to increase the availability of resources and natural gas and improve the investment attractiveness of rural areas. The 7 regions of Cluster 2 needed to develop infrastructure, public services, and agricultural production. We found the highest employment level in rural areas, the largest investments in fixed assets at the expense of municipal budget, the largest residential building area per 10,000 persons, and the largest individual residential building area in the 5 regions of Cluster 3. This study makes it possible to draw up a comprehensive regional development program and proves the need for the development of rural areas, which is especially important for the sustainable development of the Russian Federation.

Key words： Rural areas; Employment level; Labor productivity; Investments in fixed assets; Sustainable; Development Goal (SDG); Russian Federation

1. Introduction

Employment is a hot topic all over the world, especially in the face of unpredictable factors and the development and implementation of new digital technologies that have changed the structure of national economy. Official statistics showed that in the Russian Federation, the mean employment rate was 60.50% in urban areas and 52.20% in rural areas in 2020 (Bogoviz, 2021). The employment level in different rural areas of the Russian Federation ranged from 39.00% to 67.00% in 2021 (Popkova, 2022). It is still meaningful to improve the living standards of rural areas (Mondal et al., 2023).
Therefore, the following questions emerge. Firstly, how does the development of rural areas contribute to employment? The analysis of the employment level in rural areas will help the administrative agencies and the governments to propose local measures, which can improve the living standards of local people, promote the development of local economy, and reduce the imbalance of regional development (Mondal et al., 2023). Secondly, how does the place of residence and work of population determine the employment level, standards of living, and incentives to migrate? Santos and Fernández Fernández (2022) observed that it is challenging to account for the fluctuation in population over time in urban and rural areas by the impact of any single factor. It is most likely that the increase of size and population in urban and rural areas is a result of real estate decisions (Santos and Fernández Fernández, 2022). Thirdly, how does rural population choose their permanent residence based on job opportunities (Hlaváček et al., 2023)? Finally, how important is the availability of infrastructure facilities for the livelihoods and employment of rural population? Some literature used descriptive methods, economic analysis, and panel data to analyze these topics (Behar and Mok, 2019; Mogila et al., 2022; Popkova, 2022; Shestak and Savenkova, 2023).
Based on the above analyses, this study aims to develop a methodology for analyzing the employment and development levels in rural areas of the Russian Federation, as well as to establish an effective policy to promote the sustainable development of the study area.

2. Literature review

Sustainable Development Goal (SDG) 8 (Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all) was included in the United Nations SDGs for 2030 (United Nations, 2015). Providing decent work is an urgent task in almost all countries of the world. Anichin et al. (2022) noted that employment is an important factor driving economic development. Moreover, Ding et al. (2020) analyzed the empirical relationship among economic growth, labor, capital, education, health, environment, and SDGs.
Regional development needs to consider issues such as employment, living standards, and socio-economic development. Employment is essential for the socio-economic development of a country and is the basis for increasing the income. The provision of adequate employment can help to reduce income inequality of population, increase economic growth, and ensure political stability (Behar and Mok, 2019). Ding et al. (2020) found a strong dependence between industrial structure and employment structure. Economic structure includes the number of labor resources, the composition and value of fixed assets, the amount of investment in fixed capital, and the gross output by type of economic activity (Anichin et al., 2022).
Rural areas are traditionally characterized by a smaller coverage of labor and more homogeneous type of production. Káposzta and Nagy (2022) said that for many people, rural areas are simply considered as a place of agricultural activities and food production. Some scholars analyzed the employment and development levels in urban areas (Mogila et al., 2022; Popkova, 2022; Shestak and Savenkova, 2023). Studying urban and rural development and urbanization allows for the identification of disparities in region development. These disparities can be eliminated and evened out through appropriate policies. It is necessary to pursue a policy of comprehensive and coordinated development of urban and rural areas (Castillo et al., 2023).
Employment opportunities, infrastructure, and public services in rural areas are increasing. Research results demonstrate that service sector has a huge ability to absorb labor, leading to an immense impact on employment (Ding et al., 2020). Therefore, we included indicators of socio-economic development in rural areas to analyze the employment level in rural areas of the Russian Federation. Rural areas are characterized by scattered people and objects. Therefore, population mobility in rural areas was estimated to be lower than that in urban areas (Gao et al., 2023). It is necessary to develop public transport routes and use different transport modes, which can promote population mobility between urban and rural areas, increase the attractiveness of rural areas, and improve the development of rural areas (Gao et al., 2023).
Schafer and Henn (2023) raised the issues of employment of international migrants in rural areas. Research shows that international migrants are the main labors in rural areas of the Russian Federation, which is becoming typical for the country. International migrants are occupied in trade, construction, and seasonal agricultural work. Many scholars analyzed the entrepreneurship in rural areas (Wu et al., 2022; Low et al., 2023). In light of digital economy, there may be new employment opportunities in industries and small businesses. People doing business in rural areas are more committed and attached to these areas. Rural entrepreneurship contributes to the development of rural areas (Low et al., 2023). Joining cooperatives allows people to improve their financial situation and well-being (Wu et al., 2022). Therefore, the development of cooperative movement is necessary, which is beneficial for members and cooperative.
Rikalović et al. (2020) stated that it is a new topic to analyze the quality of labors in order to promote economic growth in rural areas, which has recently received more and more attention. Improving the quality of labors in rural areas is one of the main factors driving the growth of rural well-being. Thus, an empirical study confirmed a positive relationship between the percentage of people employed in creative professions and the growth rate of entrepreneurial activities in rural areas of Serbia (Rikalović et al., 2020). There is some research on the impact of the public sector on formal private sector employment in Turkey. For example, Aldan (2021) characterized the impact as an indicator of the elasticity of local labor supply. The development of rural areas predominantly lags behind that of urban areas (Castillo et al., 2023). Research revealed that there are no systematic and significant differences between urban and rural areas in terms of goods, services, process innovation, or exporting (Tiwasing et al., 2023). Mostly, the economic growth of rural areas is weaker. At the same time, start-ups, which today are the basis of economic growth and the engine of science, are mainly concentrated in urban areas. Some scholars discussed the direction of political decisions that can attract start-ups to rural areas and increase the attractiveness of rural areas for investors (Moskaleva, 2020). Research showed that digital technology start-ups can move between urban and rural areas. Developments related to digital technologies are not tied locally to customers or suppliers (Khasanov et al., 2019). The development and implementation of digital technologies make it possible for rural areas to apply for start-ups along with urban areas (Wuth, 2023). Klimanova et al. (2022) believed that it is necessary to increase productivity and transform the economic structure in the most geographically remote areas. This has led to the fact that geographically remote areas also participate in the economic growth of Sweden (Henning et al., 2023). Ingram and Maye (2023) widened the scope of inquiry about digital agriculture by introducing ‘capacities’ as a new theoretical lens to examine how institutional structures and processes shape the utilization of digital data, technologies, and their underlying directionalities. Alonso et al. (2023) tailored financial inclusion measures to population size, population ageing, employment rate, and labor dynamism.
Moreover, Gao et al. (2023) analyzed the difference between urban and rural development levels, urban and rural population living standards, and urban and rural digital economy development. Some studies compared the employment level in different regions (Mogila et al., 2022; Popkova, 2022; Shestak and Savenkova, 2023). However, an integrated approach to studying the relationship between the employment and development levels in rural areas has not been sufficiently analyzed. This article aims to study the employment level in rural areas and analyze how the development of regions affects the desire of the population to work in the Russian Federation.

3. Study area and methods

3.1. Study area

This study selected 71 regions in all federal districts of the Russian Federation with a vast territory, uneven population density, climatic diversity, and a variety of economic activities. The total number of regions selected does not include important cities of the Russian Federation, regions where natural and climatic conditions are unfavourable for agriculture, and regions where industry is predominantly practiced (northern regions). The employment and development levels in rural areas are particular relevance for the Russian Federation. The rural population of the Russian Federation is 25.30% of the total population (the rural population ranging from 14.00% to 70.00% in different regions) (Federal State Statistics Service of the Russian Federation, 2022). In 2020, the sectors with the largest employment population in the Russian Federation were wholesale, retail, and repair of motor vehicles, which accounted for 15.40% of the total employment population. Agriculture accounted for 6.00% of all employment population. The traditional economic activity in rural areas is agriculture, which is the main activity of rural population in the Russian Federation. At present, not all rural areas have large agricultural producers. Therefore, the part of rural population was engaged in small business or personal subsidiary farming (Ding et al., 2020). Hence, about 4.41×105 persons were employed in small businesses in agriculture, which was 3.90% of all engaged in small-scale business, with the turnover accounting for 1.80% in 2022. Individual entrepreneurs working in agriculture were about 1.03×105 persons, which accounted for 3.80% of all individual entrepreneurs, with turnover of 2.90% in 2022 (Federal State Statistics Service of the Russian Federation, 2022).

3.2. Indicator selection and data sources

Socio-economic indicators were collected from the National Statistical Service of Russia (Federal State Statistics Service of the Russian Federation, 2022). This study selected 15 indicators in 2021 that reflect the characteristics of socio-economic and infrastructure development in rural areas of the Russian Federation. An analytical groping of 71 regions of all federal districts of the Russian Federation was carried out. In 2021, there were 0.16×105 municipalities in 71 regions of the Russian Federation. Primary data collected by regions were aggregated and calculated at an average level by regional state statistic bodies of the Russian Federation. Average values are generalized characteristics of a population. When calculating them, information about the variation of indicator values, and the spatial distribution and location of rural areas is smoothed, generalized, and averaged. Consequently, some information about the distribution of indicator values within a population is lost.
This study used cluster analysis to divide regions and indicators. The main advantage of cluster analysis is that it can group regions according to the whole of multiple attributes or indicators at the same time, rather than an attribute. Cluster analysis allows identifying groups by a certain attribute and tracing the behaviour and level of other indicators in groups. Cluster analysis was done in several steps. The first step is the choice of an attribute by which can divide regions into different groups. There is a certain relationship between employment and development levels in rural areas. Therefore, the grouping criterion is the employment level in rural areas. In the next step, we determined the number of groups. In previous research, often 3 groups were classed: worst, average, and best (Magasumovna et al., 2017). Therefore, regions were divided into 3 groups in this study. Thirdly, this study identified the upper and lower boundaries of group and compiled an interval series of the distribution of regions according to the value of employment level in rural areas. When identifying the boundaries, this study analyzed the nature of the distribution of regions according to the grouping criterion and representativeness of groups. Based on the requirements of statistical science, a group with a simple analytical grouping should not contain more than 50.00% of the entire set of observation units. Also, the group should not be represented by an observation unit (Khasanov et al., 2020). Fourthly, a system of indicator was developed for a qualitative assessment of the selected groups, which can reflect the development level of rural areas.
Multidimensional grouping can identify the proportions and patterns of employment level in rural areas. Statistica (StatSoft GmbH, Hamburg, Germany) was used for cluster analysis. Firstly, checking whether the distribution pattern of the examined indicators conforms to the normal distribution. The probability density of a normal distribution was determined as follows:
$\varphi (x)=\frac{1}{\sigma \sqrt{2\text{ }\!\!\pi\!\!\text{ }}}\times {{\text{e}}^{-\frac{{{({{x}_{i}}-\mu )}^{2}}}{2{{\sigma }^{2}}}}}$,
where φ is the probability density of a normal distribution; х is the selected indicator; i is the number of selected indicators; σ is the standard deviation; e is the constant; and μ is the average value.
When implementing cluster analysis, it is necessary to standardize the selected indicators, which can be obtained by the following formula:
$Z=\frac{{{x}_{i}}-\mu }{\sigma }$,
where Z is the standardized indicator.
In the process of calculating the standardised indicator, the average value of variable takes on a value equal to 0, and the value of the standard deviation of indicator is 1. The initial data were converted to dimensionless form. Regions were grouped into clusters using the Manhattan distance (dManhattan), and the value of the distance between 2 regions was calculated by the following formula:
${{d}_{\text{Manhattan}}}({{\bar{x}}_{1}},{{\bar{x}}_{2}})={{\left\| {{{\bar{x}}}_{1}}-{{{\bar{x}}}_{2}} \right\|}_{1}}=\sum\nolimits_{i}{\left| x_{1}^{i}-x_{2}^{i} \right|}$,
where ${{\bar{x}}_{1}}$ and ${{\bar{x}}_{2}}$ are the average values of indicators; and $x_{1}^{i}$ and $x_{2}^{i}$ are the coordinates of indicator.
From the point of view of geographical location, similarity of territories, and the possibility of close cooperation, cluster analysis was carried out for 14 regions of the Volga Federal District in the Russian Federation. The results of cluster analysis can visually display the geographical location of clusters. The time series analysis method was used to study the dynamics of investments in fixed assets and labor productivity in the Russian Federation during 2010-2022.

4. Results

4.1. Characteristics of selected indicators

In this study, we selected 15 indicators in 71 regions to analyze the employment and development levels in rural areas of the Russian Federation. The average of each indicator was calculated to reflect the nature of each group (Table 1).
Table 1 Group results of 15 indicators in 71 regions of the Russian Federation.
Indicator Group 1
(20 regions)		 Group 2
(31 regions)		 Group 3
(20 regions)		 Average Difference between Group 1
and Group 3
Percentage of employment level in rural areas (%) 46.50 52.20 57.50 52.10 11.00
Percentage of rural population (%) 33.10 30.80 29.50 31.10 -3.60
Labor productivity in agriculture (×103 USD/person) 12.74 12.10 15.53 13.45 2.79
Percentage of population engaged in agriculture (%) 10.30 12.40 11.00 11.20 0.70
Investments in fixed assets at the expense of municipal budget (USD/person) 6.13 8.47 11.80 9.07 5.67
Number of service stores (hairdressers, laundries, sewing studios, repair shops, etc.) per 10,000 persons 10.17 10.34 9.97 10.16 -0.20
Number of retail stores per 10,000 persons 56.02 51.30 45.57 50.96 -10.45
Number of sport facilities per 10,000 persons 26.48 28.61 28.28 27.79 1.80
Number of rural settlements without natural gas per 10,000 persons 44.14 42.64 26.23 37.67 -17.91
Number of medical and preventive organizations per 10,000 persons 11.91 12.06 10.52 11.49 -1.39
Residential building area per 10,000 persons (km2/10,000 persons) 4.14 4.40 6.05 4.86 1.91
Individual residential building area (km2/person) 3.30 3.62 5.34 4.10 2.04
Number of collective tourist accommodation facilities per 10,000 persons 2.85 2.61 2.44 2.63 -0.41
Number of rural settlements served by postal service per 10,000 persons 47.92 47.40 37.86 44.40 -10.06
Number of rural settlements with telephoned per 10,000 persons 44.16 42.38 33.39 39.98 -10.77
We divided 71 regions into 3 groups. Group 1 included 20 regions, with 28.20% of the total population and the employment level less than 50.00%; Group 2 contained 31 regions, with 28.20% of the total population and the employment level varying from 50.00% to 55.00%; and Group 3 covered 20 regions, with 43.60% of the total population and the employment level more than 55.00%.
From Table 1 we can see that the percentage of employment level in rural areas increased by 5.70% between Group 1 and Group 2 and increased by 5.30% between Group 2 and Group 3. Investments in fixed assets at the expense of municipal budget, residential building area per 10,000 persons, and individual residence building had the same change trend and increased by 5.67 USD/person, 1.91 km2/10,000 persons, and 2.04 km2/person between Group 1 and Group 3, respectively. While the percentage of rural population decreased by 3.60% between Group 1 and Group 3. Number of service stores (hairdressers, laundries, sewing studios, repair shops, etc.) per 10,000 persons, number of retail stores per 10,000 persons, number of rural settlements without natural gas per 10,000 persons, number of medical and preventive organizations per 10,000 persons, number of collective tourist accommodation facilities per 10,000 persons, number of rural settlements served by postal service per 10,000 persons, and number of rural settlements with telephones per 10,000 persons also decreased by 0.20, 10.45, 17.91, 1.39, 0.41, 10.06, and 10.77 between Group 1 and Group 3, respectively.
The percentage of population engaged in agriculture increased by 2.10% between Group 1 and Group 2, then it decreased by 1.40% between Group 2 and Group 3. Number of service stores (hairdressers, laundries, sewing studios, repair shops, etc.) per 10,000 persons, number of sport facilities per 10,000 persons, and number of medical and preventive organizations per 10,000 persons have exhibited the same change trend. It was traditionally believed that agriculture is the main employment sector in rural areas, but Table 1 displayed a variety of rural employments.
Regions of Group 1 had the highest percentage of rural population (33.10%). The percentage of population engaged in agriculture had the highest value (12.40%) in Group 2. Regions of Group 3 had the highest percentage of employment level in rural areas (57.50%), the highest labor productivity in agriculture (15.53×103 USD/person), and the highest investments in fixed assets at the expense of municipal budget (11.80 USD/person). The variation trend for 15 indicators of each group demonstrated the heterogeneity of different regions in the Russian Federation, which testified to the uneven socio-economic development in the study area.

4.2. Cluster analysis of selected regions and indicators

From the point of view of geographical location, similarity of regions, and the possibility of close cooperation, we conducted cluster analysis in 14 regions of the Volga Federal District and divided these regions into 3 clusters. Dendrogram and spatial distribution of 14 regions are present in Figures 1 and 2, respectively. Cluster 1 covered Kirov Region and Republic of Mari El, accounting for 14.30% of the total population in the Volga Federal District; Cluster 2 included Ulyanovsk Region, Saratov Region, Nizhny Novgorod Region, Perm Territory, Orenburg Region, Chuvash Region, and Republic of Mordovia, accounting for 50.00% of the total population; and Cluster 3 contained Republic of Tatarstan, Samara Region, Udmurtian Republic, Penza Region, and Republic of Bashkortostan, accounting for 35.70% of the total population. Except for Cluster 1, regions in other clusters were geographically decentralized.
Fig. 1. Dendrogram of 14 regions in the Volga Federal District of the Russian Federation.
Fig. 2. Spatial distribution of clusters of 14 regions in the Volga Federal District of the Russian Federation.
Moreover, we divided 15 indicators of 14 regions in the Volga Federal District into 3 divisions based on the nature of indicators (Table 2). Division 1 combined the indicators of socio-economic development in rural areas as places of residence and work. Division 2 included the indicators of socio-economic status. Division 3 contained the indicators of investment and development of rural areas. The smallest percentage of rural population was observed in Cluster 1, while rural areas in this cluster are developing well. The 2 regions of Cluster 1 need to increase the availability of natural gas and improve the investment attractiveness of rural areas. The highest percentage of rural population (29.30%) and the largest percentage of population engaged in agriculture (12.8%) were observed in Cluster 2. The 7 regions of this cluster were recommended to develop infrastructures, public service facilities, and agricultural production, as well as build a program for socio-economic development in rural areas from the standpoint of convenience for people living here. For the 5 regions of Cluster 3, the highest employment level in rural areas, the largest investments in fixed assets at the expense of municipal budget, the largest residential building area per 10,000 persons, and the largest individual residential building area, were observed, which indicated that rural tourism and infrastructure are well developed in this cluster.
Table 2 Cluster analysis results of 15 indicators for 14 regions in the Volga Federal District.
Division Indicator Cluster 1
(2 regions)		 Cluster 2
(7 regions)		 Cluster 3
(5 regions)		 Average
Division
1		 Percentage of employment level in rural population (%) 53.20 52.30 53.60 53.03
Labor productivity in agriculture (×103 USD/person) 15.72 11.24 15.40 14.12
Number of rural settlements without natural gas per 10,000 persons 68.93 10.91 10.03 29.96
Number of collective tourist accommodation facilities per 10,000 persons 1.94 1.35 1.78 1.69
Number of rural settlements served by postal service per 10,000 persons 76.27 26.77 29.14 44.06
Number of rural settlements with telephoned per 10,000 persons 76.71 26.06 27.34 43.37
Division
2		 Percentage of rural population (%) 27.60 29.30 29.20 28.70
Percentage of population engaged in agriculture (%) 11.50 12.80 10.60 11.63
Number of retail stores per 10,000 persons 51.44 43.34 57.66 50.81
Number of sport facilities per 10,000 persons 34.49 28.41 38.40 33.77
Number of medical and preventive organisations per 10,000 persons 15.97 12.05 14.83 14.28
Division
3		 Investments in fixed assets at the expense of municipal budget (USD/person) 1.45 4.60 6.31 4.12
Number of service stores (hairdressers, laundries, sewing studios, repair shops, etc.) per 10,000 people 3.65 7.39 16.34 9.12
Residential building area per 10,000 persons (km2/10,000 persons) 3.80 4.11 8.39 5.43
Individual residential building area (km2/person) 3.49 3.75 6.88 4.71

4.3. Characteristics of investments in rural areas

Cluster analysis demonstrated that investment is one of the most important ways to improve employment level in rural areas. Sufficient investment can be used to build and improve infrastructure in rural areas. In 2020, the total investment in the economy of the Russian Federation amounted to 2.78×1012 USD. Specifically, residential buildings accounted for 12.40% of the total investment, buildings (except for residence) and land improvement got 39.20% of the total investment, machinery, equipment, and vehicles obtained 37.40% of the total investment, intellectual property occupied 4.10% of the total investment, and agriculture received 4.30% of the total investment. The financial investment of enterprises in 2020 amounted to 42.32×1012 USD, including long-term (10.60%) and short-term (89.40%) investments (Federal State Statistics Service of the Russian Federation, 2022). Thus, most of the investment was the fund in enterprises. Investments in fixed assets at the expense of municipal budget was about 0.10% of the total investment in the economy of the Russian Federation. The Russian Federation proposed a state project for the “Integrated Development of Rural Territories” during 2020-2025. Table 3 shows some expected goals of the state project.
Table 3 Expected goals of the “Integrated Development of Rural Territories” until 2025.
Goal Target value
Increasing the level of average monthly funds available to rural and urban households (%) 80.00
Increasing the percentage of well-maintained housing stock in its total volume in rural areas of the project area (%) 50.00
Protecting of rural population within the project area (%) 100.00
Providing households with access to the Internet in rural areas of the project area (%) 95.00
Ensuring the percentage of children aged 1-6 years old who receive preschool education in municipal educational organisations in the total number of children of the corresponding age in rural areas of the project area (%) 70.00
Ensuring the percentage of population systematically engaged in physical culture and sports in rural areas of the project area (%) 55.00
Ensuring the percentage of gasification of residential buildings (apartments) with network gas in rural areas of the project area (%) 72.00
Providing drinking water to a certain percentage of population within the project area (%) 80.00
Providing sewage treatment system for certain percentage of the houses within the project area (%) 65.00
Reduction of the average radius of accessibility of the feldsher metering station for rural population within the project area (km) 6.00
Reduction of the average radius of accessibility of educational institutions for rural population within the project area (km) 6.00
Obviously, investments in technology, management processes, and fixed assets can affect both the employment level and labor productivity. Labor productivity can reflect the efficiency of labors or enterprises. The dynamics of the investments in fixed assets and labor productivity in the Russian Federation from 2010 to 2022 were analyzed (Fig. 3). The investments in fixed assets and labor productivity have changed consistently during 2010-2022. However, the fluctuation of investments in fixed assets during 2010-2022 was much higher than the fluctuation of labor productivity, which directly testified the dependence of economic activity on investments.
Fig. 3. Dynamics of labor productivity and investments in fixed assets in the Russian Federation during 2010-2022.

5. Discussion

The main problems of rural areas are the low quality level of education, competence of labor force, and inadequate infrastructure (Popkova and Sergi, 2021; Popkova, 2022). On the one hand, rural areas have great potential for the development of environmentally friendly agriculture, small businesses, and processing industries. On the other hand, there is a lower employment level and an insufficient supply of qualified labor in rural areas compared to urban areas (Popkova and Sergi, 2021). People of small settlements and rural areas receive a lower level of education and demonstrate a significantly lower level of professional skills. These differences will be reduced when the level of education is improved (Zarifa et al., 2019). The development of rural areas requires a policy that is appreciably able to promote innovation capability and education level in rural areas (Káposzta and Nagy, 2022). In this study, we included the percentage of population engaged in agriculture and labor productivity in agriculture. The analysis of agricultural enterprise activities in rural areas is also supported by Kaur and Kaur (2019). They assessed the costs and benefits of on-farm crop and dairy farming and analyzed the contribution of dairy farming to individual farm income and employment. Since dairy farming is becoming important for marginalized and small farmers, it is necessary to prioritize the development of dairy enterprises to increase the income level of them (Kaur and Kaur, 2019; Popkova and Sergi, 2021). Rizov (2020) noted that the technological and scientific development in recent years has increased the efficiency of agricultural production, made agriculture more independent of weather and natural conditions, helped to produce high-quality agricultural products in different conditions, promoted the employment level in rural areas, and increased the level of wage and incomes of labors. In 2020, out of the total population employed in all economic sectors, workers with higher education accounted for 35.40% and workers with secondary vocational education accounted for 44.80% in the Russian Federation. At the same time, out of the total population employed in “agriculture, forestry, hunting, fishing, and fish farming sectors”, labors with higher education accounted for 14.50% and labors with secondary vocational education occupied 42.20% (Bogoviz, 2021). The use of technological equipment can produce more agricultural products with less labors and achieve higher labor productivity. Moreover, labors should receive a higher level of education. There is a need to further develop integrated development plans for rural areas in order to provide training in agriculture for labors in rural areas, especially for young people (Popkova and Sergi, 2021).
Bogoviz (2021) concluded that the amount of investments has significant impact on employment level. Our study showed that when investments in fixed assets at the expense of municipal budget increased by 1.00 USD/person, the employment level in rural areas would increase by 0.09%. Investments in rural areas will create job opportunity, and improve agricultural production efficiency, labor productivity, and living standards of rural population, consequently fostering growth in related industries and driving the overall rural development. The change trend of investments in fixed assets and labor productivity revealed a notable correlation. The trend line of labor productivity is close to the actual data (at 20.78%), and the trend line of investments in fixed assets is close to the actual data at 34.42% (Fig. 3).
Dillon et al. (2019) developed a test that allows to analyze and assess the state of labor market in rural areas by the correlation analysis methods. Mai et al. (2021) revealed that among the factors driving the income of rural population, the quality of labor is the most important. In 2019, the per capita monthly income of urban population amounted to 512.26 USD, and that of rural population was 324.86 USD in the Russia Federation (Bogoviz, 2021; Popkova, 2022). From the perspective of the income structure, the income from labor activities in urban areas accounted for 79.60% and the income from labor activities in rural areas accounted for 68.80%. The income of self-employment in urban areas was 6.30%, and that of self-employment in rural areas was 7.90%. The proportion of pensions in per capita income was 14.10% in urban areas and 22.30% in rural areas (Bogoviz, 2021). State programs can enhance the development level of rural areas and improve the welfare and living standards of rural population.
In this study, the determining factor driving the employment level in rural areas was investment, which can develop infrastructure and improve agricultural production efficiency in the region. Relevant departments need to formulate corresponding policies or measures to attract investment, which can promote the employment and development levels in rural areas and improve the living standards of rural population. The main problems of labor market in the Russian Federation are the low quality of labors, the shortage of specialists, the outflow of qualified personnel, etc. These problems are indirectly evidenced by the record-low unemployment rate (Shestak and Tsyplakova, 2023). However, in the future, it is expected that construction, agriculture, and information technology industry will face labor shortages (Shestak and Tsyplakova, 2023). An effective way to combat labor shortages is to improve technology and reduce the percentage of manual labors. With the rapid development of science and technology and the restructuring of industry, the demand for labor is changing very rapidly, so it is necessary to improve the adaptability and skill level of labors.

6. Conclusions

This study selected 15 indicators in 71 regions of the Russian Federation in 2021 to analyze the employment and development levels in rural areas in 2021. With an increase in employment level in rural areas, investments in fixed assets at the expense of municipal budget, residential building area per 10,000 persons, and individual residential building area increased. The percentage of rural population declined by 3.60% between Group 1 and Group 3. The percentage of population engaged in agriculture increased by 2.10% between Group 1 and Group 2, and decreased by 1.40% between Group 2 and Group 3.
Moreover, we conducted cluster analysis in 14 regions of the Volga Federal District and divided these regions into 3 clusters. Cluster 1 covered Kirov Region and Republic of Mari El; Cluster 2 included Ulyanovsk Region, Saratov Region, Nizhny Novgorod Region, Perm Territory, Orenburg Region, Chuvash Region, and Republic of Mordovia; and Cluster 3 contained Republic of Tatarstan, Samara Region, Udmurtian Republic, Penza Region, and Republic of Bashkortostan. The smallest percentage of rural population was observed in Cluster 1. The 2 regions of Cluster 1 need to increase the availability of natural gas and improve the investment attractiveness of rural areas. The 7 regions of Cluster 2 were recommended to develop infrastructures, public service facilities, and agricultural production, as well as build a program for socio-economic development in rural areas from the standpoint of convenience for people living here. Rural tourism and infrastructure were well developed in the 5 regions of Cluster 3.
The officially published data on the development of rural areas mainly include indicators of infrastructure development. The limited availability of statistical indicators in rural areas restricts the achievement of more valid and reliable results. Therefore, this study shows that there is a need to supplement indicators in order to more accurately and reliably reflect the employment and development levels in rural areas. The future work can identify key indicators and develop an up-to-date comprehensive scorecard to comprehensively monitor the development of rural areas.

Authorship contribution statement

Guzel SALIMOVA: conceptualization, data curation, writing - original draft, and writing - review & editing; Gulnara NIGMATULLINA: formal analysis and investigation; Gamir HABIROV: methodology, project administration, and resources; Alisa ABLEEVA: software, supervision, and validation; and Rasul GUSMANOV: formal analysis, project administration, and visualization. All authors approved the manuscript.

Declaration of conflict interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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